Skip to main content

Overview

The Problem: Unlock Data Lacks Consistency

Crypto projects report release schedules without any standardized enforcement. Data can be spread across blog posts, whitepapers, on-chain contracts, and multisigs, leading to ambiguity and making it difficult for users to trust the information they find.

Our Solution: Precision & Assumption

We introduce the Precision & Assumption framework. For every token we track, we show you where our data comes from (Assumption) and how accurate its timing is (Precision). This framework gives you the clarity to assess the transparency of a release schedule.
Image

Methodology

Definitions

  • Assumption (Data Source Types): Indicates where the release schedule data originates.
  • Precision (Release Time): Indicates how precise the release timing is.

Assumption (Data Sources)

Source TypeDefinition
Public Project Data & On-chain VerifiedThe release schedule was publicly announced by the project and verified by us on-chain.
Public Project DataThe release schedule was publicly announced by the project (e.g., in a blog post).
Vesting ContractThe release schedule is derived directly from a time-lock smart contract.
Private Project DataThe release schedule was confirmed privately by the project’s team.
Inferred On-chainThe release schedule is interpreted by our team from on-chain behavior.

Precision (Release Timing)

Precision LevelDefinition
SecondThe release occurs at the exact second specified.
BlockThe release is tied to a specific block number.
HourThe release can occur at any time within the specified hour.
DayThe release can occur at any time within the specified day.
WeekThe release can occur at any time within the specified week.
MonthThe release can occur at any time within the specified month.
To Be DeterminedThe release time is not yet known (see TBD Locked Supply).

Example Cases

Example 1: Vesting Contract with Exact Timestamps

  • Scenario: A vesting contract is deployed on-chain with parameters specifying exact unlock times down to the second (e.g., 2025-01-01 00:00:00 UTC).
  • Analysis: Because the contract itself dictates the precise timing, the data source is both transparent and verifiable on-chain. Users can independently confirm the schedule without ambiguity.
  • Our Categorization: Vesting Contract with precision Second (the unlock will execute exactly at or very close to that timestamp).

Example 2: Whitepaper with Monthly Unlocks

  • Scenario: A whitepaper states that 10% of tokens unlock “monthly” (e.g., January, February, March) without specifying days or hours.
  • Analysis: The data is official, but without finer time granularity. The unlock could occur on the 1st, 15th, or 30th of the month.
  • Our Categorization: Public Project Data with precision Month (the unlock can occur at any point during the specified month).

Example 3: Private Confirmation from Project

  • Scenario: The team privately shares aspects of their vesting schedule with Tokenomist but does not publish it publicly.
  • Analysis: While the source is direct, it lacks public verifiability. Confidence depends on the team’s reliability rather than on-chain guarantees.
  • Our Categorization: Private Project Data with precision Day (if the team specifies exact dates, but not timestamps).

Access Features

The Precision & Assumption framework is used across the following Tokenomist features:
  • Overview Pane — Every token page includes a Tokenomics Reference section that displays the Assumption and Precision tags for each allocation.

Using Precision & Assumption

1. Evaluate Reliability of Unlocks
  • A Cliff Unlock for an Investor allocation with a Vesting Contract + Second precision is highly reliable, near-certain.
  • An unlock with Inferred On-chain + Month precision is far less reliable and should be treated cautiously.
2. Compare Allocations Across Sources See how different allocations within a project vary in reliability — some may be contract-verified while others are only privately confirmed. 3. Incorporate into Risk Assessment Before acting on vesting data, always check its Assumption and Precision to gauge confidence.